Access Watson Natural Language in Excel

Take your understanding of unstructured data to a whole new level with a full suite of advanced text analytics features to extract entities, relationships, keywords, semantic roles and more.

Keywords

Returns important keywords in the content. Available metrics:

Keyword

Relevance Score

Count (occurances of keyword)

Sentiment label (Positive, neutral or negative)

Sentiment Score

Emotion Scores (Sadness, Joy, Fear, Disgust and Anger)

Concepts

Returns high-level concepts in the content.For example, a research paper about deep learning might return the concept, "Artificial Intelligence" although the term is not mentioned.

Emotions

Detects anger, disgust, fear, joy, or sadness that is conveyed in the content or by the context around target phrases specified in the targets parameter. Available options:

Targets - Emotion results are returned for each target string found in the document. Enter one target per newline. Leave empty to return emotions for the entire text. Remember to select the Target Field when including Targets:

Sentiment

Analyzes the general sentiment of your content or the sentiment toward specific target phrases. Available options: Targets (see above)

Entities

Identifies people, cities, organizations, and other other entities in the content. Available metrics:

Keyword

Type

SubTypes (comma-separated list)

Relevance Score

Count (occurances of keyword)

Sentiment label (Positive, neutral or negative)

Sentiment Score

Emotion Scores (Sadness, Joy, Fear, Disgust and Anger)

Categories

Returns a five-level taxonomy of the content. The top three categories are returned.

Relations

Recognizes when two entities are related and identifies the type of relation. See this page for a list of relations. Available metrics: